Multi-modal Egocentric Activity Recognition using Audio-Visual Features

TL;DR

This paper introduces a novel multi-modal framework combining audio-visual features with multi-kernel learning and boosting for egocentric activity recognition, demonstrating superior performance over existing methods.

Contribution

The work presents a new adaptive fusion framework using MKL and MKBoost for egocentric activity recognition with multi-modal features, improving accuracy.

Findings

MKL outperforms traditional fusion methods

Multi-modal features improve recognition accuracy

Framework tested on multiple egocentric datasets

Abstract

Egocentric activity recognition in first-person videos has an increasing importance with a variety of applications such as lifelogging, summarization, assisted-living and activity tracking. Existing methods for this task are based on interpretation of various sensor information using pre-determined weights for each feature. In this work, we propose a new framework for egocentric activity recognition problem based on combining audio-visual features with multi-kernel learning (MKL) and multi-kernel boosting (MKBoost). For that purpose, firstly grid optical-flow, virtual-inertia feature, log-covariance, cuboid are extracted from the video. The audio signal is characterized using a "supervector", obtained based on Gaussian mixture modelling of frame-level features, followed by a maximum a-posteriori adaptation. Then, the extracted multi-modal features are adaptively fused by MKL classifiers in which both the feature and kernel selection/weighing and recognition tasks are performed together. The proposed framework was evaluated on a number of egocentric datasets. The results showed that using multi-modal features with MKL outperforms the existing methods.